Editor's note: Michael S. Garver is professor of marketing at Central Michigan University, Mt. Pleasant, Mich.
In boardrooms all over the world, executives want to know what product and service attributes and features are most important to customers. With this information in hand, executives can tailor their offering to best meet the needs of customers to gain a competitive advantage in the marketplace.
There are a number of marketing research techniques available to help practitioners prioritize the importance of attributes, yet many of these research techniques have serious flaws and limitations. Chrzan and Golovashkina (2006) examined a number of the common research methods for eliciting attribute importance and they suggest that commonly-used methods such as stated importance or preference rating scales have severe limitations and often result in biased and misleading results. Rank-ordering and constant sum are intuitively appealing, yet these methods prove to be difficult when the list of attributes exceeds five or seven, which is often the case for research practitioners. These researchers suggest that maximum difference scaling and Q-sort are the best research methods for prioritizing the importance of attributes.
Chrzan and Golovashkina (2006) suggest that Q-sort is an excellent research method for gathering customer preferences for a long list of potential customer needs or proposed product features. These researchers found that Q-sort outperformed all research methods except for maximum difference scaling. At the same time, Q-sort took the least amount of customer time to complete the survey, whereas maximum difference scaling took the longest time to complete. In addition, maximum difference scaling can only be implemented with special software which may not be available to all researchers. In short, Q-sort is an excellent alternative to maximum difference scaling. However, Q-sort make...